add spotlignt.mp4

index.html

@@ -148,7 +148,7 @@ <h1 class="title is-1 publication-title">SLR: Learning Quadruped Locomotion with
                 muted
                 preload
                 playsinline height="100%">
-      <source src="./static/videos/main_noauthor.mp4"
+      <source src="./static/videos/spotlight.mp4"
                 type="video/mp4">
       </video>
       <div>
@@ -171,7 +171,7 @@ <h2 class="subtitle has-text-centered">
       <div class="column is-four-fifths">
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
-          Traditional reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of our proposed method's evaluation, SLR is compared with open-source code repositories of state-of-the-art algorithms, retaining the original authors' configuration parameters. Across four repositories, SLR consistently outperforms the reference results. Ultimately, the trained policy and encoder empower the quadruped robot to navigate steps, climb stairs, ascend rocks, and traverse various challenging terrains.
+          The recent mainstream reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. 
         </div>
       </div>
     </div>